What does Veles Digital build?

Custom AI tools, automation pipelines, conversational chatbots, and analytics dashboards. We engineer practical AI systems that eliminate waste, accelerate growth, and ship. Not demos. Every project is shaped around the specific workflows of a client's business.

How long does a custom AI project take?

A typical first-version delivery is two to eight weeks. Discovery and specification take about a week, a focused MVP runs two to four weeks, and larger integrations or analytics dashboards extend to six to eight weeks. We ship working software in short cycles rather than long-lead waterfalls.

Do you work with non-technical clients?

Yes. Most of our clients are not engineers. We translate business problems into AI solutions, handle the technical complexity end-to-end, and deliver tools your team can actually use. Onboarding includes training and clear documentation.

What industries does Veles Digital serve?

We're industry-agnostic. The common thread across our clients is teams who want concrete AI-driven outcomes like automation, better analytics, and customer support efficiency, rather than experimental AI projects. Each engagement is shaped around your specific workflows and operational reality.

How does a project start?

Book a free 30-minute discovery call via calendly.com/velesdigitalsolutions/30min, message us on WhatsApp at +381 69 251 1196, or email contact@veles.digital. We discuss your business, identify which workflows AI can improve, and propose a scoped first project with a fixed price and timeline. We respond within 24 hours.

Where is Veles Digital based and who do you serve?

Veles Digital is headquartered in Serbia and serves clients across Europe and worldwide. We operate fully remotely with English and Serbian language support, and most engagements happen via async tooling and scheduled video calls.

How much does custom AI development cost?

We don't publish fixed prices because every project differs in scope, integrations, and timeline. The discovery call is free, runs 30 minutes, and ends with a scoped quote based on your specific needs. Typical engagements range from focused two-to-four-week MVPs to six-to-eight-week multi-system integrations, with fixed scope and fixed price for the agreed phase.

What's the difference between Veles Digital and a ChatGPT subscription?

ChatGPT is a general-purpose assistant; Veles Digital builds production AI shaped around your specific workflows, integrated with your data, deployed on your own stack. ChatGPT helps individuals draft emails. We build systems that automate your support inbox, qualify your inbound leads, surface insights from your sales calls, or pull together a board-ready dashboard from your real-time data. Owned by you, not rented per seat from OpenAI.

Which AI platforms and models do you build on?

We're model-agnostic. We work with OpenAI (GPT-4o, GPT-4.1), Anthropic Claude (Sonnet, Opus, Haiku), Google Gemini, Mistral, and open-source models (Llama, Qwen) depending on the use case. Selection is driven by cost, latency, accuracy, and data-residency requirements, not vendor loyalty. We also use vector databases (Qdrant, pgvector, Pinecone) and orchestration frameworks (LangChain, custom agentic patterns) where they make sense.

Can you integrate AI with our existing CRM, ERP, or data warehouse?

Yes. Integration is the core of every project. We connect AI capabilities to Salesforce, HubSpot, Pipedrive, Notion, Airtable, Postgres, MySQL, BigQuery, Snowflake, and most modern SaaS APIs. If your stack has a documented API or webhook surface, we can wire AI into it. We don't ask you to migrate your data to a new platform.

Do you provide ongoing AI maintenance and updates?

Yes. After delivery, we offer optional retainers covering production monitoring, model upgrades, prompt tuning, and feature evolution. Most clients move to retainer after the initial build to keep their AI current as models and APIs evolve. What worked on GPT-4o today may be obsolete or expensive six months from now, and a small ongoing engagement keeps the system competitive.

How do you measure AI ROI for our business?

Every project starts with a measurable target like hours saved per week, response time reduction, conversion lift, support ticket deflection, or qualified leads per month. We instrument the AI system to report on those metrics from day one. If we can't measure the win, we don't ship it. Discovery includes a baseline measurement so the impact is provable, not anecdotal.

// AI Analytics

AI analytics dashboards that answer the question you keep asking your team

Custom analytics surfaces that pull from your real data sources (CRM, spreadsheets, SaaS APIs, custom databases), apply AI where it actually helps (anomaly detection, segmentation, scoring), and surface the three numbers that matter today. No vanity metrics, no 200-tile dashboards no one reads.

What we build

Veles builds custom AI analytics dashboards that solve one specific business question per surface. Not a generic BI tool with a thousand charts. A focused, role-specific dashboard that tells you what to do next.

The "AI" in the name does real work: anomaly detection on revenue or traffic, candidate or lead scoring, customer segmentation, natural-language summaries of what changed week over week. Where AI helps, we use it. Where a sorted table is the right answer, we ship a sorted table.

Typical analytics deliverables:

Operator dashboard for the founder or general manager. The three or four metrics that drive the decision they are about to make this week
Sales dashboard with pipeline health, lead scoring, and a ranked list of accounts that need a human touch today
Customer-success dashboard with churn risk scoring, usage anomaly flags, and account-by-account context
Recruitment scoring dashboard (see People Up): AI ranks candidates against role criteria, human-readable explanation per score, sortable + filterable by recruiter
Owner dashboard (see Mobilni Market): one-screen view of inventory, orders, low-stock alerts, and a "what to promote tomorrow" suggestion from the AI

Where we pull data from

We connect to the messy reality of your stack. Most analytics projects fail because the data lives in five different places and no one wants to wire them up. We do that part.

SaaS APIs: HubSpot, Salesforce, Stripe, Shopify, Notion, Slack, Calendly, ManyChat, Mailchimp, anything with a documented API or webhook
Your databases: Postgres, MySQL, MongoDB, Snowflake, BigQuery, or whatever your engineering team already runs
Spreadsheets: the Google Sheet your finance person updates every Monday is a perfectly valid data source. We read it directly
Custom systems: in-house back-office software, legacy ERPs, ticketing tools that "do not have an API" (they usually do, just buried)
Manual entry: when no API exists, we build a clean entry surface so the data stays current

How it works

1. Discovery. A 30-minute call. You walk us through the decision you are trying to make. We ask the questions that turn "I want a dashboard" into "I want to know X by Wednesday so I can do Y."

2. Scope and estimate. We map the dashboard into milestones: which data sources, which AI features (scoring, anomaly detection, segmentation), which views, which alerts. Fixed-price estimate. See our scoping methodology.

3. Build. Weekly demos. By week 2 you usually have a working dashboard with mock data; by week 4 it is reading from real sources; by week 6 it is in production with the alerts wired up.

4. Launch and operate. We deploy, monitor for two weeks, and tune the alert thresholds against real traffic. After that, hand off the code or stay on a monthly retainer that includes dashboard tweaks, new metric additions, and integration maintenance when third-party APIs change.

Why custom over Looker / Tableau / Metabase

Off-the-shelf BI tools (Looker Studio, Tableau, Metabase, Mode, Power BI) are great when:

You have a data warehouse already
Your team has a dedicated analyst who lives in the tool
The questions are well-defined and stable

They fall apart when:

Your data lives in 5 SaaS products + a Google Sheet + a Postgres database, and no warehouse glues them
The AI features you want (scoring, anomaly detection, NL summaries) are not in the tool's default feature set
You want a role-specific dashboard, not "another BI tool to learn"
You want to own the code and stop paying per-seat fees forever

Custom AI analytics dashboards from Veles are the right answer when off-the-shelf BI tools are either too generic or too operator-unfriendly for the people who need to use them.

Who we work with

Founders and operators who want one dashboard that tells them what to do today, not a BI license they will never log into
B2B teams with pipeline + customer-success data spread across HubSpot, Stripe, and a CSM's spreadsheet, who need a unified view
Recruitment and HR teams that want AI-assisted scoring without buying a $50k ATS
E-commerce operators who want a dashboard that combines orders, inventory, and customer messages in one place (see Mobilni Market)

// Proof from production

Already running in production

Recruitment OS

People Up

Recruitment scoring dashboard. AI ranks candidates against the role, generates human-readable explanations per score, recruiter sees a sorted shortlist and the why behind each rank. Multi-day screening pipelines now finish in hours.

Case study

AI Commerce Stack

Mobilni Market

Owner dashboard for a B2B wholesaler. One-screen view of orders, low-stock alerts, and an AI-generated "promote this tomorrow" suggestion. The owner makes one decision a day, the data ranks everything else.

Case study

AI Booking System

Kacimbas Autostyling

Booking analytics behind the Instagram AI agent: which questions convert, which DM channels drive the most bookings, which hours of day have the highest conversion rate. The data feeds back into agent tuning.

Case study

// FAQ

Frequently asked

What makes an analytics dashboard "AI" instead of just a regular dashboard?

The AI does specific work: scoring (rank candidates / leads / customers by some criteria), anomaly detection (flag the week revenue dipped 18% so you do not miss it), segmentation (group customers by behavior, not by a column you have to maintain), and natural-language summaries ("conversions are down because mobile traffic dropped 22% on Wednesday"). Where AI does not add value, the dashboard just shows you the numbers. We do not bolt on AI to claim a feature.

Will the dashboard work with the tools we already use?

Yes. We connect to HubSpot, Salesforce, Stripe, Shopify, Notion, Slack, Postgres, MongoDB, Snowflake, BigQuery, Google Sheets, and anything with an API or webhook. If your data lives in a custom back-office tool, we wire that up too. You do not migrate to a new platform to use this.

How is this different from Looker Studio or Metabase?

Off-the-shelf BI tools are general-purpose. They assume you have a data warehouse and a dedicated analyst. Custom AI analytics from Veles is the opposite: we build one focused dashboard for one specific decision, pull from your messy real-world data sources directly, and ship the AI features (scoring, anomaly detection, NL summaries) that BI tools do not have by default. You also own the code and stop paying per-seat fees.

How long does an AI analytics build take?

Most builds are 2-6 weeks. A focused single-metric dashboard with 1-2 data sources lands in 2 weeks. A multi-source operator dashboard with AI scoring + anomaly alerts runs 4-6 weeks. We give you a concrete timeline at the end of Discovery.

Can we add new metrics after launch without paying you again?

Yes. The code is yours and documented. If you have an engineer on the team, they can add new charts, new metrics, and new data sources without us. If you do not, we are available on a monthly retainer that includes regular additions and tweaks. Most clients start by adding one or two metrics themselves after a couple of weeks once they see how the dashboard is structured.

What about data privacy? Our metrics include sensitive customer info.

We are GDPR-compliant (EU-based, Belgrade). Dashboards we build can run entirely inside your infrastructure if you prefer, with no data leaving your environment. For projects with sensitive workloads (financial records, healthcare-adjacent, employee data), we run inference on-premise or via privacy-preserving providers. Specifics get nailed down in a Data Processing Addendum before production data moves.

Can the dashboard send alerts when something important changes?

Yes. We wire up alerts to Slack, email, WhatsApp, or your custom channel. Common patterns: revenue anomaly alert (drop or spike beyond 2 standard deviations), pipeline risk alert (no movement on a high-value deal for X days), low-stock alert, churn-risk alert. The alert tells you what to do, not just that something is unusual.

What does an analytics project typically cost?

Depends on data sources and AI features. A single-source dashboard without complex AI scoring is on one end of our range. A multi-source dashboard with custom scoring, anomaly detection, and a Slack alert layer is on the other. After a 30-minute discovery call you get a real fixed-price estimate. See our scoping methodology for how we get there.

How we estimate

No tiered pricing because every dashboard build varies on data sources and AI features. After Discovery we map the dashboard into milestones, identify the integrations and the hard parts (the spreadsheet finance refuses to migrate, the legacy system whose API documentation lies, etc.), and give you a concrete estimate. No surprises.

See our scoping methodology →

Talk to us about your dashboard

Bring the question you keep asking your team and the data sources that should answer it. 30 minutes is enough to scope a real dashboard or to tell you Looker Studio would do the job for free.

book a discovery call Or email us